Method and System for Role Dependent Context Sensitive Spoken and Textual Language Understanding with Neural Networks

ABSTRACT

A method and system processes utterances that are acquired either from an automatic speech recognition (ASR) system or text. The utterances have associated identities of each party, such as role A utterances and role B utterances. The information corresponding to utterances, such as word sequence and identity, are converted to features. Each feature is received in an input layer of a neural network(NN). A dimensionality of each feature is reduced, in a projection layer of the NN, to produce a reduced dimensional feature. The reduced dimensional feature is processed to provide probabilities of labels for the utterances.

FIELD OF THE INVENTION

This invention relates generally to dialog processing, and more particularly to Natural Language Understanding (NLU) methods and systems for dialogs including spoken and textual utterances.

BACKGROUND OF THE INVENTION

Methods and systems of Natural Language Understanding (NLU), which can perform, for example, Spoken Language Understanding (SLU), are used in computerized dialog systems to estimate intentions of utterances. As broadly defined herein, the “spoken” utterances can be in the form of speech or text. If the utterances are spoken, then the uterterances can be obtained from, for example, an automatic speech recognition (ASR) system. If the utterances are text, then the utterances can be obtained from, e.g., a text processing systems or keyboard input.

Conventional intention estimation methods can be based on phrase matching, or classification methods, such as boosting, support vector machines (SVM), and Logistic Regression(LR) using Bag of Word (BoW) features of each utterance as inputs. However, the BoW features do not have enough capability to indicate semantic information represented by word sequeces due to, for example, missing order of words in the sequences.

To consider a history of a word sequence in each utterance, a Recurrent Neural Networks (RNNs) can be applied for utterance classification using 1-of-N coding instead of the BoW features. Addtionally, Long Short-Term Memory (LSTM) RNNs are a form of RNNs designed to improve learning of long-range context, and can be effective for context dependent problems. Those of approaches classify utterances without considering context among utterances. Addtionally, it is essential to consider a broader context of a sequence of utterances of an entire dialog to understand intention accurately. Some of the prior art models using RNNs and LSTMs use word sequence context within a single utterance and also consider a broader context of a sequence of utterances of an entire dialog.

Furthermore, each utterance has different expressions in terms of context of party-dependent features such as for task-oriented roles like agents and clients, business dependent terminolgies and expressions, gender dependent languages, relationships among participants in the dialogs. However, conventional methods do not consider such party-dependent features due to the different roles.

SUMMARY OF THE INVENTION

The embodiments of the invention provide a method and system for processing utterances. The utterances are acquired either from an automatic speech recognition (ASR) system or text. The utterances have associated identities of each party, such as role A utterances and role B utterances. The information corresponding to utterances, such as word sequence and identity, are converted to features. Each feature is received in an input layer of a neural network(NN). A dimensionality of each feature is reduced, in a projection layer of the NN, to produce a reduced dimensional feature. The reduced dimensional feature is processed, where the feature is propagated through hidden layers. In case of recurrent neural network (RNN), hidden layers have reccurent connections and long short-term memory (LSTM) can be applied to hidden layers of the RNN. Then, in an output layer of the NN, posterior probabilities of labels are determined.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic of party-dependent neural networks with a shared context history of an entire dialog among two parties;

FIG. 1B is a schematic of party-dependent expressions through different layers of a single neural network;

FIG. 1C is a schematic of a context sensitive spoken language understanding (SLU) method and system in the form of a recurrent neural network with two parallel hidden, long short-term memory (LSTM) layers according to embodiments of the invention;

FIG. 2 is a schematic of set of LSTM cells in the hidden layers according to embodiments of the invention;

FIG. 3 is a schematic of a propagation process of the context-sensitive SLU according to embodiments of the invention;

FIG. 4 is a schematic of details of the two parallel LSTM layers according to embodiments of the invention; and

FIG. 5 is a schematic of temporal process of the role-dependent SLU according to embodiemnts of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The embodiments of our invention provide a method and system for language understanding, e.g., a spoken language understanding (SLU). The method and can estimate intentions of utterances in a dialog. As broadly defined herein, the utterances can be in the form of speech or text. If the utterances are spoken, then the uterterances can be obtained from, for example, an automatic speech recognition (ASR) system. If the utterances are text, then the utterances can be obtained from, e.g., a text processing systems or keyboard input.

Context-Sensitive SLU Using NNs

FIG. 1A shows party-dependent neural networks 100 with a shared context history of an entire dialog among two parties for role A 101 on the left, and role B 102 on the right, respectively. This embodiment of the invention uses two neural networks (NNs), which considering party-dependent expressions with or without long-term context over a dialog.

FIG. 1B shows party-dependent expressions through different layers of a single neural network 100. The variables used in the figures are described detail below.

FIG. 1C schematically shows the RNN 100 in an alternative form. Here, the input layer 110 receives input word vectors 111 from the ASR 105. The word vectors correspond to utterances acquired from a client 101 and an agent 102. Typically, the client and agent take turns speaking the utterances during their respective roles A and B.

The method and networks can be inplemented in a processor connected to memory and input/output interfaces as known in the art.

By convention, each network is shown with the input layer 110 at the bottom, and the output layer 140 at the top. The input layer 110 receives input word vectors 111 corresponding to the utterances by multiple parties. The utterances have associated identities of each party. The identities relate to the roles performed by the parties. The word vectors correspond to utterances acquired for role A 101, e.g., a client party, and role B 102, e.g., an agent party. Typically, the parties take turns generating the utterances for each role during a dialog.

A projection layer 120 reduces the dimensionality of the word vector to produce a reduced dimensional word vector. A recurrent hidden layer 130 is constructed as long short-term memory (LSTM) with recurrent connections with party gates 131 that can retain and forget context information. The LSTM layers produce activation vectors for the uterrances. The output layer 140 estimates posterior probabilities of output labels 141 based on the activation vectors.

To understand the intentions in a dialog of the mutiple parties accurately, it is important to consider party-dependent expressions in each utterance and the function of each utterance in the context of a sequence of dialog turns as well.

To consider both context of an entire dialog and party-dependent expressions of each utterance, we provide an efficient NLU approach based on neural networks (NN) that model the context sensitive party-dependent expressions through either party-dependent neural and networks shared context history among parties shown in FIG. 1A, or a single neural network facilitated with party-dependent different layers as shown in FIG. 1B.

Each word is input sequentially into the NNs using either word vector representation such as BoW or 1-of-N coding with or without features of additional atrributes such as sematic, syntactic, task-oriented information. The features of word sequence are propageted through one of the party-dependent hidden layers, and semantic information, such as concept tags, are output at the end of each utterance. Concept tags only represent symbols. Semantic information can be symbols, and/or structured information such as a graph.

In case of the RNN, to propagate contextual information through a dialog, the activation vector of the RNN for an utterance serves as input to the RNN for the next utterance to consider contex of an entire dialog. The embodiments train the RNN layers of a context sensitive model to predict sequences of semantic information from the word sequences with considering party-dependent expressions.

The utterances in the dialog corpus are characterized for each party in terms of roles, such as agent or client. In order to precisely model the party-dependent utterances, we provide multiple party-dependent neural networks which are shared context history of an entire dialog among parties as shown FIG. 1A. In addition, we provide a single neural network facilitated with party-dependent different layers depicted in FIG. 1B.

The different party-dpendent features are modeled by switching between the party-dependent hidden layers. In these models, words of each utterance are input one at a time, and semantic information are output at the end of each utterance. In case of the RNN, the party-dependent hidden layers jointly represent both the context within each utterance, and the context within the dialog. In other NNs, the pary-dependent hidden layers represent only the characteristics of each party's utterance.

As shown in FIG. 2, we use a set of LSTM cells 200 in the hidden layer 130, instead of network units as in conventional RNNs. The LSTM cells can remember a value for an arbitrary length of time using gates. The LSTM cell contains input 210, forget 220, and output 230 gates, which respectively determine when the input is significant enough to remember, when to forget the input, and when the input contributes to the output.

A sequence of M utterances is u₁, . . . , u_(τ), . . . , u_(M). Each utterance u_(m) includes a word sequence w_(τ,1), . . . , w_(τ,t), . . . , w_(τ,T) _(τ) and a concept tag a_(τ). The input word vectors x_(τ,t) 111 received from the ASR are

x _(τ,t)=OneHot(w _(τ,t)),   (1)

where the word w_(τ,t) in a vocabulary V is converted by 1-of-N coding using a function OneHot(w), i.e., x_(τ,t) ∈ {0,1}^(|v|).

The input vector is projected by the projection layer 120 to a D dimensional vector

x _(τ,t) ′=W _(pr) x _(τ,t) +b _(pr),   (2)

which is then fed to the recurrent hidden layers 130, where W_(pr) is a projection matrix, and b_(pr) is a bias vector.

At the hidden layers 130, activation vectors h_(τ,t) are determined using the LSTM cells according

i _(τ,t)=σ(W _(xi) x′ _(τ,t) +W _(hi) h _(τ,t−1) +W _(ci) c _(τ,t−1) +b _(i)),   (3)

f _(τ,t)=σ(W _(xf) x′ _(τ,t) +W _(hf) h _(τ,t−1) +W _(cf) c _(τ,t−1) +b _(f)),   (4)

c _(τ,t) =f _(τ,t) c _(τ,t−1) +i _(τ,t) tan h(W _(xc) x′ _(τ,t) +W _(hc) h _(τ,t−1) +b _(c)),   (5)

o _(τ,t)=σ(W _(xo) x′ _(τ,t) +W _(ho) h _(τ,t−1) +W _(co) c _(τ,t) +b _(o)), and   (6)

h _(τ,t) =o _(τ,t) tan h(c _(τ,t)),   (7)

where σ( ) is an element-wise sigmoid function, and i_(τ,t), f_(τ,t), o_(τ,t) and c_(96 ,t) are the input gate 210, forget gate 220, output gate 230, and cell activation vectors for the t^(th) input word in the x-th utterance, respectively.

Weight matrices W_(zz) and bias vectors b_(z) are identified by a subscript z ∈ {x, h, i, f,o, c}. For example, W_(hi) is a hidden-input gate matrix, and W_(xo) is an input-output gate matrix.

The output vector 141 is determined at the end of each utterance as

y _(τ)=softmax(W _(HO) h _(τ,T) _(τ) +b _(O)),   (8)

where W_(HO) is a transformation matrix, and b_(O) is a bias vector to classify the input vector into different categories according to the hidden vector. Softmax( ) is an element-wise softmax function that converts the classification result into label probabilities, i.e., y_(τ) ∈ [0,1]^(|L|) for label set L

$\begin{matrix} {{{\hat{a}}_{\tau} = {\underset{a \in \mathcal{L}}{\arg \; \max}\; {y_{\tau}\lbrack a\rbrack}}},_{\;}} & (9) \end{matrix}$

where y_(τ)[a] indicates the component of y_(τ) for label a, which corresponds to the probability P (a|h_(τ,T) _(τ) ) of the label.

To inherit the context information from previous utterances, the hidden and cell activation vectors at the beginning of each utterance are

h _(τ,0) =h _(τ−1,T) _(τ−1)   (10)

c_(τ,0) =c _(τ−1,T) _(τ−1′)   (11)

where τ>1 and h_(1,0)=c_(1,0)=0, as shown in FIG. 2.

FIG. 3 shows a propagation process of our context-sensitive SLU. Words w_(i,j) are sequentially input to the LSTM layers 130, and a label 141 that is output corresponds to the utterance concept at the end of the utterance, where symbol EOS represents “end of sentence.”

In contrast with the prior art, our model considers the entire context from the beginning to the end of the dialog. Accordingly, the label probabilities can be inferred using sentence-level intentions an dialog-level context. In contrast, the conventional models only considers each utterance independently.

Role-Dependent LSTM Layers

The LSTM layers can be trained using a human-to-human dialog corpus annotated with concept tags, which represent, e.g., client and agent intentions for a hotel reservation as shown in Table 1 below. The columns, left to right, indicate the speaker, e.g., agent and client, uterances, and concept tags. The uterrances are characterized by each role of agent and client.

TABLE 1 An Example of Hotel Reservation Dialog Speaker Utterance Concept tags Agent hello, greeting Agent new york city hotel, introduce-self Agent may i help you? offer + help Cleint i would like to make a request-action + reservation + hotel reservation for a room, Agent very good, acknowledge Agent may i have the spelling request-information + name of your name, please? Client it is mike, smith, give-information + name Agent uh when would you like request-information + temporal to stay? Client from august tenth to give-information + temporal august sixteenth, seven days, Agent i see, acknowledge Agent you would be arriving on verify-give-information + temporal the tenth? is the right? Client that is right, affirm Agent great, acknowledge Agent and, what sort of room request-information + room would you like? Client well, it is just for myself, give-information + party Client so a single room would give-information + room be good, Agent okay, acknowledge Agent a single, verify-give-information + room Agent starting on the tenth and verify-give-information + temporal Agent you would be checking verify-give-information + temporal out on the sixteenth? is that right? Client yes, affirm Client and i like the second of give-information + room third floor, if possible, Agent i see, acknowledge

As shown in FIG. 4, two parallel LSTM layers 310 and 320 representing client (A) and agent (B) utterances are incorporated in the model. Role gates 311 and 321 control which role, client or agent, is active.

The two LSTM layers have different parameters depending on the speaker roles. Thus, the input vector 111 for the client utterances is processed by the layer 310, and by the layer 320 processes the agent utterances. The active role for a given utterance is controlled by a role variable R, which is used to gate the output to each LSTM layer.

Then, the gated outputs are passed from the recurrent LSTM layers to the output layer 140. The recurrent LSTM inputs thus receive the output from the role-dependent layer active at the previous frame, allowing for transitions between roles.

Error signals in the training phase are back-propagated through the corresponding layers. The role of each speaker does not change during a dialog and the speaker of each utternace is known. However, the model structure leaves open the possibility of dynamically inferring the roles. Accordingly, we can determine the activation at the output layer as

y _(τ)=softmax(δ_(R,R) _(τ) (W_(HO) h _(τ,T) _(τ) ^((R)) +b _(O))),   (12)

where h_(τ,T) _(τ) ^((R)) is the hidden activation vector given by the LSTM layer of role R, and δ_(R,R) _(τ) is Kronecker's delta, i.e., if R_(τ) is the role of the x-th utterance equals role R, R is 1, otherwise R is 0. At the beginning of each utterance, the hidden and cell activation vectors of the role-dependent layer are

h _(τ,0) ^((R) ^(τ) ⁾ =h _(τ−1,T) _(τ−1) ^((R) ^(τ−1) ⁾, and   (13)

c _(τ,0) ^((R) ^(τ) ⁾ =c _(τ−1,T) _(τ−1) ^((R) ^(τ−1) ⁾.   (14)

FIG. 5 shows the temporal process of the role-dependent SLU. For each utterance in a given role, only the LSTM layer for that role is active, and the hidden activation and the cell memory are propagated over dialog turns. The figure shows the client utterances (Role A), and the agent utterances (Role B). With this architecture, both LSTM layers can be trained considering a long context of each dialog, and the model can predict role-dependent concept labels accurately.

Effect of the Invention

The invention provides an efficient context sensitive SLU using role-based LSTM layers. In order to determine long-term characteristics over an entire dialog, we implemented LSTMs representing intention using consequent word sequences of each concept tag. We have evaluated the performance of importing contextual information of an entire dialog for SLU and the effectiveness of the speaker role based LSTM layers. The context sensitive LSTMs with roll-dependent layers out-performs utterance-based approaches and improves the SLU baseline by 11.6% and 8.0% (absolute).

Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention. 

1. A method for processing utterances, comprising steps: acquiring utterances from multiple parties as word sequences, wherein each of the utterances has an associated identity of each party; converting the word sequences and identities to features; receiving, in an input layer of a neural network (NN), each feature; reducing, in a projection layer of the NN, a dimensionality of each feature to produce a reduced dimensional feature; processing, the reduced dimensional feature to propagate the feature through hidden layers of the NN; and determining, in an output layer of the NN, posterior probabilities of labels for the utterances, wherein the steps are performed in a processor.
 2. The method of claim 1, wherein the NN is a recurrent NN (RNN), or a RNN with a long short-term memory (LSTM) in hidden layers of the NN with recurrent connections in the hidden layers.
 3. The method of claim 1, wherein the utterances are spoken, and further comprising: converting the utterances to the word sequences in an automatic speech recognition system (ASR).
 4. The method of claim 1, wherein the utterances are text to form the word sequences.
 5. The method of claim 1, wherein the NN is party-dependent neural networks with a shared context history of an entire dialog among the multiple parties.
 6. The method of claim 1, wherein party-dependent expressions are used through the layers of the NN.
 7. The method of claim 1, wherein the utterances form a dialog, and a context of the dialog is considered, and the probabilities of the labels are inferred using sentence-level intentions and the context of the dialog.
 8. The method of claim 2, wherein the LSTM includes recurrent connections with party gates that retain and forget context information.
 9. The method of claim 2, wherein the LSTM includes cells remember a value for an arbitrary length of time using the party gates.
 10. The method of claim 1, wherein words in the word sequences and features are processed sequentially, and the features include semantic, syntactic, and task-oriented attributes.
 11. The method of claim 1, wherein the features are propageted through party-dependent hidden layers, and semantic information including concept tags are output at an end of each utterance, and wherein the concept tags only represent symbols, and the semantic information includes symbols and structured information.
 12. The method of claim 1, wherein the utterances are characterized in terms of roles of the multiple parties.
 13. The method of claim 8, wherein the party gates control which one of the multiple parties is active 